{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matFilename = './Data/2017-06-30_batchdata_updated_struct_errorcorrect.mat'\n",
    "f = h5py.File(matFilename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#refs#', '#subsystem#', 'batch', 'batch_date']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(f.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "batch = f['batch']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Vdlin',\n",
       " 'barcode',\n",
       " 'channel_id',\n",
       " 'cycle_life',\n",
       " 'cycles',\n",
       " 'policy',\n",
       " 'policy_readable',\n",
       " 'summary']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(batch.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "num_cells = batch['summary'].shape[0]\n",
    "bat_dict = {}\n",
    "for i in range(num_cells):\n",
    "    cl = f[batch['cycle_life'][i,0]].value\n",
    "    policy = f[batch['policy_readable'][i,0]].value.tobytes()[::2].decode()\n",
    "    summary_IR = np.hstack(f[batch['summary'][i,0]]['IR'][0,:].tolist())\n",
    "    summary_QC = np.hstack(f[batch['summary'][i,0]]['QCharge'][0,:].tolist())\n",
    "    summary_QD = np.hstack(f[batch['summary'][i,0]]['QDischarge'][0,:].tolist())\n",
    "    summary_TA = np.hstack(f[batch['summary'][i,0]]['Tavg'][0,:].tolist())\n",
    "    summary_TM = np.hstack(f[batch['summary'][i,0]]['Tmin'][0,:].tolist())\n",
    "    summary_TX = np.hstack(f[batch['summary'][i,0]]['Tmax'][0,:].tolist())\n",
    "    summary_CT = np.hstack(f[batch['summary'][i,0]]['chargetime'][0,:].tolist())\n",
    "    summary_CY = np.hstack(f[batch['summary'][i,0]]['cycle'][0,:].tolist())\n",
    "    summary = {'IR': summary_IR, 'QC': summary_QC, 'QD': summary_QD, 'Tavg':\n",
    "                summary_TA, 'Tmin': summary_TM, 'Tmax': summary_TX, 'chargetime': summary_CT,\n",
    "                'cycle': summary_CY}\n",
    "    cycles = f[batch['cycles'][i,0]]\n",
    "    cycle_dict = {}\n",
    "    for j in range(cycles['I'].shape[0]):\n",
    "        I = np.hstack((f[cycles['I'][j,0]].value))\n",
    "        Qc = np.hstack((f[cycles['Qc'][j,0]].value))\n",
    "        Qd = np.hstack((f[cycles['Qd'][j,0]].value))\n",
    "        Qdlin = np.hstack((f[cycles['Qdlin'][j,0]].value))\n",
    "        T = np.hstack((f[cycles['T'][j,0]].value))\n",
    "        Tdlin = np.hstack((f[cycles['Tdlin'][j,0]].value))\n",
    "        V = np.hstack((f[cycles['V'][j,0]].value))\n",
    "        dQdV = np.hstack((f[cycles['discharge_dQdV'][j,0]].value))\n",
    "        t = np.hstack((f[cycles['t'][j,0]].value))\n",
    "        cd = {'I': I, 'Qc': Qc, 'Qd': Qd, 'Qdlin': Qdlin, 'T': T, 'Tdlin': Tdlin, 'V':V, 'dQdV': dQdV, 't':t}\n",
    "        cycle_dict[str(j)] = cd\n",
    "        \n",
    "    cell_dict = {'cycle_life': cl, 'charge_policy':policy, 'summary': summary, 'cycles': cycle_dict}\n",
    "    key = 'b2c' + str(i)\n",
    "    bat_dict[key]=   cell_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['b2c0', 'b2c1', 'b2c2', 'b2c3', 'b2c4', 'b2c5', 'b2c6', 'b2c7', 'b2c8', 'b2c9', 'b2c10', 'b2c11', 'b2c12', 'b2c13', 'b2c14', 'b2c15', 'b2c16', 'b2c17', 'b2c18', 'b2c19', 'b2c20', 'b2c21', 'b2c22', 'b2c23', 'b2c24', 'b2c25', 'b2c26', 'b2c27', 'b2c28', 'b2c29', 'b2c30', 'b2c31', 'b2c32', 'b2c33', 'b2c34', 'b2c35', 'b2c36', 'b2c37', 'b2c38', 'b2c39', 'b2c40', 'b2c41', 'b2c42', 'b2c43', 'b2c44', 'b2c45', 'b2c46', 'b2c47'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bat_dict.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23595a9d4a8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bat_dict['b2c43']['summary']['cycle'], bat_dict['b2c43']['summary']['QD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x23531658da0>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bat_dict['b2c43']['cycles']['10']['Qd'], bat_dict['b2c43']['cycles']['10']['V'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open('batch2.pkl','wb') as fp:\n",
    "        pickle.dump(bat_dict,fp)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py_36_env",
   "language": "python",
   "name": "py_36_env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
